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<strong>IMPORTANT</strong>: No additional bug fixes or documentation updates
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<span class="breadcrumb-node">Create inference trained model API</span>
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<div class="section xpack">
<div class="titlepage"><div><div>
<h2 class="title">
<a id="put-inference"></a>Create inference trained model API<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a><a class="xpack_tag" href="/subscriptions"></a>
</h2>
</div></div></div>

<p>Creates an inference trained model.</p>
<div class="warning admon">
<div class="icon"></div>
<div class="admon_content">
<p>This functionality is experimental and may be changed or removed completely in a future release. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features.</p>
</div>
</div>
<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="ml-put-inference-request"></a>Request<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h3>
</div></div></div>
<p><code class="literal">PUT _ml/inference/&lt;model_id&gt;</code></p>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="ml-put-inference-prereq"></a>Prerequisites<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h3>
</div></div></div>
<p>If the Elasticsearch security features are enabled, you must have the following
built-in roles and privileges:</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
<code class="literal">machine_learning_admin</code>
</li>
</ul>
</div>
<p>For more information, see <a class="xref" href="security-privileges.html" title="Security privileges">Security privileges</a> and <a class="xref" href="built-in-roles.html" title="Built-in roles">Built-in roles</a>.</p>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="ml-put-inference-desc"></a>Description<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h3>
</div></div></div>
<p>The create inference trained model API enables you to supply a trained model that
is not created by data frame analytics.</p>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="ml-put-inference-path-params"></a>Path parameters<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h3>
</div></div></div>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">&lt;model_id&gt;</code>
</span>
</dt>
<dd>
(Required, string)
The unique identifier of the trained inference model.
</dd>
</dl>
</div>
</div>

<div class="section child_attributes">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="ml-put-inference-request-body"></a>Request body<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h3>
</div></div></div>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">compressed_definition</code>
</span>
</dt>
<dd>
(Required, string)
The compressed (GZipped and Base64 encoded) inference definition of the model.
If <code class="literal">compressed_definition</code> is specified, then <code class="literal">definition</code> cannot be specified.
</dd>
</dl>
</div>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">definition</code>
</span>
</dt>
<dd>
(Required, object)
The inference definition for the model. If <code class="literal">definition</code> is specified, then
<code class="literal">compressed_definition</code> cannot be specified.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">definition</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">preprocessors</code>
</span>
</dt>
<dd>
(Optional, object)
Collection of preprocessors.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">preprocessors</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">frequency_encoding</code>
</span>
</dt>
<dd>
(Required, object)
Defines a frequency encoding for a field.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">frequency_encoding</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">feature_name</code>
</span>
</dt>
<dd>
(Required, string)
The name of the resulting feature.
</dd>
<dt>
<span class="term">
<code class="literal">field</code>
</span>
</dt>
<dd>
(Required, string)
The field name to encode.
</dd>
<dt>
<span class="term">
<code class="literal">frequency_map</code>
</span>
</dt>
<dd>
(Required, object map of string:double)
Object that maps the field value to the frequency encoded value.
</dd>
</dl>
</div>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">one_hot_encoding</code>
</span>
</dt>
<dd>
(Required, object)
Defines a one hot encoding map for a field.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">one_hot_encoding</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">field</code>
</span>
</dt>
<dd>
(Required, string)
The field name to encode.
</dd>
<dt>
<span class="term">
<code class="literal">hot_map</code>
</span>
</dt>
<dd>
(Required, object map of strings)
String map of "field_value: one_hot_column_name".
</dd>
</dl>
</div>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">target_mean_encoding</code>
</span>
</dt>
<dd>
(Required, object)
Defines a target mean encoding for a field.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">target_mean_encoding</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">default_value</code>
</span>
</dt>
<dd>
(Required, double)
The feature value if the field value is not in the <code class="literal">target_map</code>.
</dd>
<dt>
<span class="term">
<code class="literal">feature_name</code>
</span>
</dt>
<dd>
(Required, string)
The name of the resulting feature.
</dd>
<dt>
<span class="term">
<code class="literal">field</code>
</span>
</dt>
<dd>
(Required, string)
The field name to encode.
</dd>
<dt>
<span class="term">
<code class="literal">target_map</code>
</span>
</dt>
<dd>
(Required, object map of string:double)
Object that maps the field value to the target mean value.
</dd>
</dl>
</div>
</div>
</details>
<p>See <a class="xref" href="put-inference.html#ml-put-inference-preprocessor-example" title="Preprocessor examples">Preprocessor examples</a> for more details.</p>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">trained_model</code>
</span>
</dt>
<dd>
(Required, object)
The definition of the trained model.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">trained_model</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">tree</code>
</span>
</dt>
<dd>
(Required, object)
The definition for a binary decision tree.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">tree</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">classification_labels</code>
</span>
</dt>
<dd>
(Optional, string) An array of classification labels (used for
<code class="literal">classification</code>).
</dd>
<dt>
<span class="term">
<code class="literal">feature_names</code>
</span>
</dt>
<dd>
(Required, string)
Features expected by the tree, in their expected order.
</dd>
<dt>
<span class="term">
<code class="literal">target_type</code>
</span>
</dt>
<dd>
(Required, string)
String indicating the model target type; <code class="literal">regression</code> or <code class="literal">classification</code>.
</dd>
<dt>
<span class="term">
<code class="literal">tree_structure</code>
</span>
</dt>
<dd>
(Required, object)
An array of <code class="literal">tree_node</code> objects. The nodes must be in ordinal order by their
<code class="literal">tree_node.node_index</code> value.
</dd>
</dl>
</div>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">tree_node</code>
</span>
</dt>
<dd>
(Required, object)
The definition of a node in a tree.
</dd>
</dl>
</div>
<p>There are two major types of nodes: leaf nodes and not-leaf nodes.</p>
<div class="ulist itemizedlist">
<ul class="itemizedlist">
<li class="listitem">
Leaf nodes only need <code class="literal">node_index</code> and <code class="literal">leaf_value</code> defined.
</li>
<li class="listitem">
All other nodes need <code class="literal">split_feature</code>, <code class="literal">left_child</code>, <code class="literal">right_child</code>,
<code class="literal">threshold</code>, <code class="literal">decision_type</code>, and <code class="literal">default_left</code> defined.
</li>
</ul>
</div>
<details open>
<summary class="title">Properties of <code class="literal">tree_node</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">decision_type</code>
</span>
</dt>
<dd>
(Optional, string)
Indicates the positive value (in other words, when to choose the left node)
decision type. Supported <code class="literal">lt</code>, <code class="literal">lte</code>, <code class="literal">gt</code>, <code class="literal">gte</code>. Defaults to <code class="literal">lte</code>.
</dd>
<dt>
<span class="term">
<code class="literal">default_left</code>
</span>
</dt>
<dd>
(Optional, boolean)
Indicates whether to default to the left when the feature is missing. Defaults
to <code class="literal">true</code>.
</dd>
<dt>
<span class="term">
<code class="literal">leaf_value</code>
</span>
</dt>
<dd>
(Optional, double)
The leaf value of the of the node, if the value is a leaf (in other words, no
children).
</dd>
<dt>
<span class="term">
<code class="literal">left_child</code>
</span>
</dt>
<dd>
(Optional, integer)
The index of the left child.
</dd>
<dt>
<span class="term">
<code class="literal">node_index</code>
</span>
</dt>
<dd>
(Integer)
The index of the current node.
</dd>
<dt>
<span class="term">
<code class="literal">right_child</code>
</span>
</dt>
<dd>
(Optional, integer)
The index of the right child.
</dd>
<dt>
<span class="term">
<code class="literal">split_feature</code>
</span>
</dt>
<dd>
(Optional, integer)
The index of the feature value in the feature array.
</dd>
<dt>
<span class="term">
<code class="literal">split_gain</code>
</span>
</dt>
<dd>
(Optional, double) The information gain from the split.
</dd>
<dt>
<span class="term">
<code class="literal">threshold</code>
</span>
</dt>
<dd>
(Optional, double)
The decision threshold with which to compare the feature value.
</dd>
</dl>
</div>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">ensemble</code>
</span>
</dt>
<dd>
(Optional, object)
The definition for an ensemble model.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">ensemble</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">aggregate_output</code>
</span>
</dt>
<dd>
(Required, object)
An aggregated output object that defines how to aggregate the outputs of the
<code class="literal">trained_models</code>. Supported objects are <code class="literal">weighted_mode</code>, <code class="literal">weighted_sum</code>, and
<code class="literal">logistic_regression</code>.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">aggregate_output</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">logistic_regression</code>
</span>
</dt>
<dd>
(Optional, object)
This <code class="literal">aggregated_output</code> type works with binary classification (classification
for values [0, 1]). It multiplies the outputs (in the case of the <code class="literal">ensemble</code>
model, the inference model values) by the supplied <code class="literal">weights</code>. The resulting
vector is summed and passed to a
<a href="https://en.wikipedia.org/wiki/Sigmoid_function" class="ulink" target="_top"><code class="literal">sigmoid</code> function</a>. The result
of the <code class="literal">sigmoid</code> function is considered the probability of class 1 (<code class="literal">P_1</code>),
consequently, the probability of class 0 is <code class="literal">1 - P_1</code>. The class with the
highest probability (either 0 or 1) is then returned. For more information about
logistic regression, see
<a href="https://en.wikipedia.org/wiki/Logistic_regression" class="ulink" target="_top">this wiki article</a>.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">logistic_regression</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">weights</code>
</span>
</dt>
<dd>
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
</dd>
</dl>
</div>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">weighted_sum</code>
</span>
</dt>
<dd>
(Optional, object)
This <code class="literal">aggregated_output</code> type works with regression. The weighted sum of the
input values.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">weighted_sum</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">weights</code>
</span>
</dt>
<dd>
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
</dd>
</dl>
</div>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">weighted_mode</code>
</span>
</dt>
<dd>
(Optional, object)
This <code class="literal">aggregated_output</code> type works with regression or classification. It takes
a weighted vote of the input values. The most common input value (taking the
weights into account) is returned.
</dd>
</dl>
</div>
<details open>
<summary class="title">Properties of <code class="literal">weighted_mode</code></summary>
<div class="content">
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">weights</code>
</span>
</dt>
<dd>
(Required, double)
The weights to multiply by the input values (the inference values of the trained
models).
</dd>
</dl>
</div>
</div>
</details>
<p>See <a class="xref" href="put-inference.html#ml-put-inference-aggregated-output-example" title="Aggregated output example">Aggregated output example</a> for more details.</p>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">classification_labels</code>
</span>
</dt>
<dd>
(Optional, string)
An array of classification labels.
</dd>
<dt>
<span class="term">
<code class="literal">feature_names</code>
</span>
</dt>
<dd>
(Optional, string)
Features expected by the ensemble, in their expected order.
</dd>
<dt>
<span class="term">
<code class="literal">target_type</code>
</span>
</dt>
<dd>
(Required, string)
String indicating the model target type; <code class="literal">regression</code> or <code class="literal">classification.</code>
</dd>
<dt>
<span class="term">
<code class="literal">trained_models</code>
</span>
</dt>
<dd>
(Required, object)
An array of <code class="literal">trained_model</code> objects. Supported trained models are <code class="literal">tree</code> and
<code class="literal">ensemble</code>.
</dd>
</dl>
</div>
</div>
</details>
<p>See <a class="xref" href="put-inference.html#ml-put-inference-model-example" title="Model examples">Model examples</a> for more details.</p>
</div>
</details>
</div>
</details>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">description</code>
</span>
</dt>
<dd>
(Optional, string)
A human-readable description of the inference trained model.
</dd>
<dt>
<span class="term">
<code class="literal">input</code>
</span>
</dt>
<dd>
<p>
(Required, object)
The input field names for the model definition.
</p>
<div class="variablelist">
<dl class="variablelist">
<dt>
<span class="term">
<code class="literal">input</code>.<code class="literal">field_names</code>
</span>
</dt>
<dd>
(Required, string)
An array of input field names for the model.
</dd>
</dl>
</div>
</dd>
<dt>
<span class="term">
<code class="literal">metadata</code>
</span>
</dt>
<dd>
(Optional, object)
An object map that contains metadata about the model.
</dd>
<dt>
<span class="term">
<code class="literal">tags</code>
</span>
</dt>
<dd>
(Optional, string)
An array of tags to organize the model.
</dd>
</dl>
</div>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h3 class="title">
<a id="ml-put-inference-example"></a>Examples<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h3>
</div></div></div>
<div class="section">
<div class="titlepage"><div><div>
<h4 class="title">
<a id="ml-put-inference-preprocessor-example"></a>Preprocessor examples<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h4>
</div></div></div>
<p>The example below shows a <code class="literal">frequency_encoding</code> preprocessor object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">{
   "frequency_encoding":{
      "field":"FlightDelayType",
      "feature_name":"FlightDelayType_frequency",
      "frequency_map":{
         "Carrier Delay":0.6007414737092798,
         "NAS Delay":0.6007414737092798,
         "Weather Delay":0.024573576178086153,
         "Security Delay":0.02476631010889467,
         "No Delay":0.6007414737092798,
         "Late Aircraft Delay":0.6007414737092798
      }
   }
}</pre>
</div>
<p>The next example shows a <code class="literal">one_hot_encoding</code> preprocessor object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">{
   "one_hot_encoding":{
      "field":"FlightDelayType",
      "hot_map":{
         "Carrier Delay":"FlightDelayType_Carrier Delay",
         "NAS Delay":"FlightDelayType_NAS Delay",
         "No Delay":"FlightDelayType_No Delay",
         "Late Aircraft Delay":"FlightDelayType_Late Aircraft Delay"
      }
   }
}</pre>
</div>
<p>This example shows a <code class="literal">target_mean_encoding</code> preprocessor object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">{
   "target_mean_encoding":{
      "field":"FlightDelayType",
      "feature_name":"FlightDelayType_targetmean",
      "target_map":{
         "Carrier Delay":39.97465788139886,
         "NAS Delay":39.97465788139886,
         "Security Delay":203.171206225681,
         "Weather Delay":187.64705882352948,
         "No Delay":39.97465788139886,
         "Late Aircraft Delay":39.97465788139886
      },
      "default_value":158.17995752420433
   }
}</pre>
</div>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h4 class="title">
<a id="ml-put-inference-model-example"></a>Model examples<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h4>
</div></div></div>
<p>The first example shows a <code class="literal">trained_model</code> object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">{
   "tree":{
      "feature_names":[
         "DistanceKilometers",
         "FlightTimeMin",
         "FlightDelayType_NAS Delay",
         "Origin_targetmean",
         "DestRegion_targetmean",
         "DestCityName_targetmean",
         "OriginAirportID_targetmean",
         "OriginCityName_frequency",
         "DistanceMiles",
         "FlightDelayType_Late Aircraft Delay"
      ],
      "tree_structure":[
         {
            "decision_type":"lt",
            "threshold":9069.33437193022,
            "split_feature":0,
            "split_gain":4112.094574306927,
            "node_index":0,
            "default_left":true,
            "left_child":1,
            "right_child":2
         },
         ...
         {
            "node_index":9,
            "leaf_value":-27.68987349695448
         },
         ...
      ],
      "target_type":"regression"
   }
}</pre>
</div>
<p>The following example shows an <code class="literal">ensemble</code> model object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">"ensemble":{
   "feature_names":[
      ...
   ],
   "trained_models":[
      {
         "tree":{
            "feature_names":[],
            "tree_structure":[
               {
                  "decision_type":"lte",
                  "node_index":0,
                  "leaf_value":47.64069875778043,
                  "default_left":false
               }
            ],
            "target_type":"regression"
         }
      },
      ...
   ],
   "aggregate_output":{
      "weighted_sum":{
         "weights":[
            ...
         ]
      }
   },
   "target_type":"regression"
}</pre>
</div>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h4 class="title">
<a id="ml-put-inference-aggregated-output-example"></a>Aggregated output example<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h4>
</div></div></div>
<p>Example of a <code class="literal">logistic_regression</code> object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">"aggregate_output" : {
  "logistic_regression" : {
    "weights" : [2.0, 1.0, .5, -1.0, 5.0, 1.0, 1.0]
  }
}</pre>
</div>
<p>Example of a <code class="literal">weighted_sum</code> object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">"aggregate_output" : {
  "weighted_sum" : {
    "weights" : [1.0, -1.0, .5, 1.0, 5.0]
  }
}</pre>
</div>
<p>Example of a <code class="literal">weighted_mode</code> object:</p>
<div class="pre_wrapper lang-js">
<pre class="programlisting prettyprint lang-js">"aggregate_output" : {
  "weighted_mode" : {
    "weights" : [1.0, 1.0, 1.0, 1.0, 1.0]
  }
}</pre>
</div>
</div>

<div class="section">
<div class="titlepage"><div><div>
<h4 class="title">
<a id="ml-put-inference-json-schema"></a>Inference JSON schema<a class="edit_me edit_me_private" rel="nofollow" title="Editing on GitHub is available to Elastic" href="https://github.com/elastic/elasticsearch/edit/7.7/docs/reference/ml/df-analytics/apis/put-inference.asciidoc">edit</a>
</h4>
</div></div></div>
<p>For the full JSON schema of model inference,
<a href="https://github.com/elastic/ml-json-schemas" class="ulink" target="_top">click here</a>.</p>
</div>

</div>

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